Startups are exploiting AI’s hazy definition to cash in on the hype

What exactly is artificial intelligence? In much the same way that you’d be a bit stumped if someone demanded that you provide a hard and fast definition of say, philosophy, there isn’t a satisfactorily rigorous answer to this question.

As the Stanford Encyclopaedia explains, AI’s definition falls under the category of “remarkably difficult, maybe even eternally unanswerable, questions, especially if the target is a consensus definition”. But that hasn’t stopped startups from jumping on the buzzword bandwagon. In fact, it’s helped.

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To get around this problem, most AI definitions settle for a muddled approach. A recent parliamentary paper, for instance, after conceding that there was no single definition to be found, opted for a colourful “word cloud” of definitions in the shape of a brain, where words that appeared more often were larger. Another, more useful, definition, comes from MIT Tech Review, which defines AI by what it can and cannot do (can it hear, is it transcribing what you say?) in a 21 Questions-style quiz.

“Most people don’t know what AI means”, says Julio Amador Diaz Lopez, a professor of business at Imperial College London. “Technically if I use a rules-based system, I can say that my system is intelligent, and people who know about AI may frown upon it, but they cannot technically say that I’m lying.” Technically, even a simple calculator could be considered AI.

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It’s in this context, then, that we should understand a recent report into the spread of AI hype through European startups. The research, by London-based venture capital firm MMC Ventures, found that 40 per cent of 2,830 purported AI startups in Europe don’t actually use any AI in their products. So much for the AI revolution.

Part of this deception is about riding the wave of pervasive media attention AI receives, a typical corporate eagerness not to miss the train. “It’s a buzzword,” says Lopez. “Everyday there is a bunch of news about AI, like AI beating humans in doing this and that. It's just really easy to jump into that trend.” The report also shows that simply labelling yourself as an AI company attracts a median of 15 per cent more funding than another, non-AI-touting tech startup.

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The report’s main, predictable finding is that AI startups are on the rise: back in 2013 only about 50 new startups in Europe claimed to have anything to do with AI. Now, one in 12 say AI is, to use the report’s terminology, “at the heart of their value proposition”. There’s a general urge to get involved, but a clear shortage of AI talent, says Lopez. “There are 26 million software developments in the world, but only about 300,000 people with the skills to develop AI,” he adds.

The urge to ride the AI wave has led to some rather shady behaviour. Back in 2017, for example, expense management app Expensify admitted that it was often humans, not the company’s purported “smartscan technology”, that had been processing client receipts.

Yet it is also the difficulty of defining AI that makes this deception easier. Any company that uses big data and is in the business of making predictions could reasonably argue that they use AI, says Marcin Kacperczyk, a professor of finance at Imperial. “In the corporate sector pretty much everyone uses big data, and most of the companies are in the business of making predictions. So to the extent that you can collate the two to be part of your business you can of course say you're AI.”

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MMC, for its part, says its definition was “modern AI, ie machine learning – systems that improve with experience/training instead of following rigid sets of rules”. Yet machine learning can encompass stuff as simple as predictive texting, to the algorithm that fills your Netflix suggestions with documentaries about serial killers, to the DeepMind AI system that defeated a human at Go.

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According to the report, even companies that were genuinely AI-based often used pretty basic technology, like fraud analytics or simple chatbots. As a result, companies don’t need to be using more sophisticated AI to claim they’re AI-based. “That is a legal aspect of it, if someone could be called for lying, they could always say: ‘Look, this is also AI in our definition of it,’” says Kacperczyk.

The venture firm argues that the distinction between AI and non AI-based companies will eventually become irrelevant. Until then, this type of opportunism shouldn’t come as a surprise. There was a similar obsession surrounding business intelligence in the 90s, and data science five years ago. All hyped technologies generate lucrative buzzwords. It just happens that AI has created a buzz that’s particularly easy to exploit.